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A Review of Integration Strategies to Support Gene Regulatory Network Construction
Gene regulatory network (GRN) construction is a central task of systems biology. Integration of different data sources to infer and construct GRNs is an important consideration for the success of this effort. In this paper, we will discuss distinctive strategies of data integration for GRN construct...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
The Scientific World Journal
2012
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3543788/ https://www.ncbi.nlm.nih.gov/pubmed/23365519 http://dx.doi.org/10.1100/2012/435257 |
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author | Chen, Hailin VanBuren, Vincent |
author_facet | Chen, Hailin VanBuren, Vincent |
author_sort | Chen, Hailin |
collection | PubMed |
description | Gene regulatory network (GRN) construction is a central task of systems biology. Integration of different data sources to infer and construct GRNs is an important consideration for the success of this effort. In this paper, we will discuss distinctive strategies of data integration for GRN construction. Basically, the process of integration of different data sources is divided into two phases: the first phase is collection of the required data and the second phase is data processing with advanced algorithms to infer the GRNs. In this paper these two phases are called “structural integration” and “analytic integration,” respectively. Compared with the nonintegration strategies, the integration strategies perform quite well and have better agreement with the experimental evidence. |
format | Online Article Text |
id | pubmed-3543788 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2012 |
publisher | The Scientific World Journal |
record_format | MEDLINE/PubMed |
spelling | pubmed-35437882013-01-30 A Review of Integration Strategies to Support Gene Regulatory Network Construction Chen, Hailin VanBuren, Vincent ScientificWorldJournal Review Article Gene regulatory network (GRN) construction is a central task of systems biology. Integration of different data sources to infer and construct GRNs is an important consideration for the success of this effort. In this paper, we will discuss distinctive strategies of data integration for GRN construction. Basically, the process of integration of different data sources is divided into two phases: the first phase is collection of the required data and the second phase is data processing with advanced algorithms to infer the GRNs. In this paper these two phases are called “structural integration” and “analytic integration,” respectively. Compared with the nonintegration strategies, the integration strategies perform quite well and have better agreement with the experimental evidence. The Scientific World Journal 2012-12-27 /pmc/articles/PMC3543788/ /pubmed/23365519 http://dx.doi.org/10.1100/2012/435257 Text en Copyright © 2012 H. Chen and V. VanBuren. https://creativecommons.org/licenses/by/3.0/ This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Review Article Chen, Hailin VanBuren, Vincent A Review of Integration Strategies to Support Gene Regulatory Network Construction |
title | A Review of Integration Strategies to Support Gene Regulatory Network Construction |
title_full | A Review of Integration Strategies to Support Gene Regulatory Network Construction |
title_fullStr | A Review of Integration Strategies to Support Gene Regulatory Network Construction |
title_full_unstemmed | A Review of Integration Strategies to Support Gene Regulatory Network Construction |
title_short | A Review of Integration Strategies to Support Gene Regulatory Network Construction |
title_sort | review of integration strategies to support gene regulatory network construction |
topic | Review Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3543788/ https://www.ncbi.nlm.nih.gov/pubmed/23365519 http://dx.doi.org/10.1100/2012/435257 |
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